Recent advances in online advertising have most prominently been in the field of behavioral targeting. Both web sites and networks tailor their online experiences to individuals or classes of individuals through behavioral targeting. When employed by advertising networks (“ad networks”), behavioral targeting matches advertisers that have a certain desired target audience with websites that have been profiled to draw a specific audience. One of the challenges in behavioral targeting is determining the true extent of the match between a desired audience and the actual audience drawn by a specific web page.
Online social networks, such as the Facebook service provided by Facebook, Inc. of Palo Alto, Calif., are ad networks that have very good knowledge of the visitors to specific pages within the online social network. In order to visit a page within a social network, one typically needs to be a member of the social network. In addition, members of social networks typically provide demographic information and information concerning interests in order to personalize their behavior. For example, a Facebook member could indicate that they are interested in ski vacations to Lake Tahoe by clicking a “Like” button featured on a Lake Tahoe website. A simple advertising strategy would be to target the members of an online social network who have previously indicated interest in the product or service being offered by the advertisement. A flaw with this strategy, however, is that many members that are interested in the advertised offer are not being targeted, because they have not previously indicated a desire for the products or services. More sophisticated advertising strategies attempt to build a demographic and/or geographic profile of a member that will be interested in the advertised offer and target the advertising to members matching the demographic and/or geographic profile. A campaign can be further targeted using keywords to narrow the audience for an ad to people who have interests, which correlate with the advertised offer. In many ad networks, advertisers can bid on keywords. Therefore, targeting users associated with a first keyword can cost significantly more money than targeting users associated with a second keyword. Returning to the example of an advertiser of ski travel packages to Lake Tahoe, the question becomes: who are others that may be interested in a Lake Tahoe vacation package beyond those that have specifically expressed an interest in such a vacation? Probably those who like specific ski resorts would be good candidates, and possibly also those who like specific ski manufacturers. What about those who like gambling? Since the Lake Tahoe area also features a number of casinos, the desired audience for the offer could include members that like to ski and also like to play poker. But not all who like to play poker are good candidates for such a vacation package, and as such advertisement budgets may not be wisely spent on such an audience. Therefore, challenges exist in selecting keywords that appropriately target specific offers to members of a social network.